diff --git a/app.py b/app.py index d6e70a4b95ebac238b55a1fad06176aa2c6661c7..5d93b7e285c349fd449efd39aaee75329f933364 100644 --- a/app.py +++ b/app.py @@ -74,7 +74,7 @@ iface = gr.Interface( inputs=gr.Image(type="pil", label="Upload an Image"), outputs=gr.Textbox(label="Detection & Classification Results"), title="Car Detector + Classifier (YOLO)", - description="(Status: Beta) To Test Upload any car image and get its color, model, and confidence score." + description="(Status: Beta) To Test, Upload any car image and get its color, model, and confidence score." ) if __name__ == "__main__": diff --git a/space_repo/space_repo/README.md b/space_repo/space_repo/README.md index d8e66056798a5cd9afb96e274950467b894ba5c2..3cc543d72f7b468b7b3023b2901d6e64884b9514 100644 --- a/space_repo/space_repo/README.md +++ b/space_repo/space_repo/README.md @@ -1,5 +1,5 @@ --- -title: Car Classifier Model +title: CarClassifierModel emoji: πŸš— colorFrom: blue colorTo: purple diff --git a/space_repo/space_repo/app.py b/space_repo/space_repo/app.py index 79b3b0ea42568c3ba31588bcb3ea0e1cbd3eae9e..d6e70a4b95ebac238b55a1fad06176aa2c6661c7 100644 --- a/space_repo/space_repo/app.py +++ b/space_repo/space_repo/app.py @@ -74,7 +74,7 @@ iface = gr.Interface( inputs=gr.Image(type="pil", label="Upload an Image"), outputs=gr.Textbox(label="Detection & Classification Results"), title="Car Detector + Classifier (YOLO)", - description="Upload a car image and get its color, model, and confidence score." + description="(Status: Beta) To Test Upload any car image and get its color, model, and confidence score." ) if __name__ == "__main__": diff --git a/space_repo/space_repo/space_repo/space_repo/README.md b/space_repo/space_repo/space_repo/space_repo/README.md index a37ce2635d29ab40ecb81e234d09fe48637f8bf5..d8e66056798a5cd9afb96e274950467b894ba5c2 100644 --- a/space_repo/space_repo/space_repo/space_repo/README.md +++ b/space_repo/space_repo/space_repo/space_repo/README.md @@ -4,7 +4,7 @@ emoji: πŸš— colorFrom: blue colorTo: purple sdk: gradio -sdk_version: "4.0.0" +sdk_version: "5.49.1" app_file: app.py pinned: false --- diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index df776d573fb006813537930270bfd2e2f7a4b643..79b3b0ea42568c3ba31588bcb3ea0e1cbd3eae9e 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -14,155 +14,8 @@ import gradio as gr from PIL import Image from datasets import load_dataset -# --- Ensure YOLO notebook exists --- -if not os.path.exists("YOLO.ipynb"): - raise FileNotFoundError("YOLO.ipynb not found in app directory!") - -# --- Read notebook --- -with open("YOLO.ipynb", "r", encoding="utf-8") as f: - nb = nbformat.read(f, as_version=4) - -# --------------------------- -# Robust dynamic extraction from YOLO.ipynb (safer) -# --------------------------- -import re, runpy - -with open("YOLO.ipynb", "r", encoding="utf-8") as f: - nb_all = nbformat.read(f, as_version=4) - -# Patterns that indicate a cell is important (we'll include any cell that matches any marker) -IMPORTANT_MARKERS = [ - r"torch\.hub\.load\(", # yolo loader cell - r"models\.resnet18", # classifier architecture - r"model\.load_state_dict", # checkpoint load - r"transform\(", # transform definition - r"transforms\.", # torchvision transforms usage - r"def get_color_name", # color helper - r"def detect_and_classify", # the pipeline function (must be preserved) - r"import numpy", # numpy import - r"import torch", # torch import - r"from torchvision", # torchvision imports - r"from PIL import", # PIL imports - r"import cv2", # optional cv usage -] - -# Lines we will strip from included cells (Colab magics / file upload demos) -BAD_LINE_PATTERNS = [ - r'^\s*!', # shell commands - r'^\s*%', # magics - r'google\.colab', - r'files.upload', - r'\buploaded\b', - r'\bimg_path\b', -] - -# Collect unique cells that match any important marker or imports -selected_cells = [] -seen_indices = set() -for i, cell in enumerate(nb_all.cells): - if cell.cell_type != "code": - continue - src = cell.source or "" - # include if any important marker matches - if any(re.search(p, src, flags=re.I) for p in IMPORTANT_MARKERS): - if i not in seen_indices: - seen_indices.add(i) - selected_cells.append((i, src)) - -# If we didn't find the function, as a fallback include any cell that looks like it contains function words -if not any("def detect_and_classify" in src for (_, src) in selected_cells): - for i, cell in enumerate(nb_all.cells): - if cell.cell_type != "code": - continue - src = cell.source or "" - if any(k in src.lower() for k in ["def detect", "def classify", "def predict", "detect_and_classify"]): - if i not in seen_indices: - seen_indices.add(i) - selected_cells.append((i, src)) - -# Also ensure import cells appear first -import_cells = [] -for i, cell in enumerate(nb_all.cells): - if cell.cell_type != "code": - continue - src = cell.source or "" - if re.search(r'^\s*(import |from )', src, flags=re.M): - if i not in seen_indices: - import_cells.append((i, src)) - seen_indices.add(i) - -# Sort selected cells by original order: imports first, then selected_cells by index -selected_cells_sorted = sorted(import_cells + selected_cells, key=lambda tup: tup[0]) - -# Clean each selected cell, but keep any cell that contains the function header intact. -cleaned_cells = [] -for idx, src in selected_cells_sorted: - # If this cell contains the function definition, keep it largely intact - if "def detect_and_classify" in src: - lines = [] - for line in src.splitlines(): - # remove only Colab magics / shell commands, but keep everything else - if re.match(r'^\s*!', line) or re.match(r'^\s*%', line): - continue - # keep the def line and body exactly as-is otherwise - lines.append(line) - cleaned_src = "\n".join(lines).rstrip() - if cleaned_src: - cleaned_cells.append(cleaned_src) - continue - - # Otherwise, perform conservative cleaning: remove magics, uploads, and *top-level* test calls - cleaned_lines = [] - for line in src.splitlines(): - # drop Colab magics / shell commands and file picker demo lines - if any(re.search(p, line) for p in BAD_LINE_PATTERNS): - continue - # drop top-level calls to detect_and_classify() in non-function cells (prevents auto-run) - if re.search(r'\bdetect_and_classify\s*\(', line): - continue - cleaned_lines.append(line) - cleaned_src = "\n".join(cleaned_lines).rstrip() - if cleaned_src: - cleaned_cells.append(cleaned_src) - -# Join cells into script (imports / helpers first by sorted index) -safe_code = "\n\n# ---- cell boundary ----\n\n".join(cleaned_cells) - -# Ensure numpy is available if used -if not re.search(r'(^|\n)\s*(import numpy\b|import numpy as\b|from numpy\b)', safe_code): - safe_code = "import numpy as np\n\n" + safe_code - -# Sanitize stray placeholders -safe_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', safe_code, count=1, flags=re.M) -safe_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', safe_code, flags=re.M) - -# Write converted file -with open("yolo_converted.py", "w", encoding="utf-8") as fh: - fh.write(safe_code) - -print("Wrote yolo_converted.py β€” preview:") -print(safe_code[:1000].replace("\n", "\\n")) -# --------------------------- -# End dynamic extraction block -# --------------------------- - -# --- Try to import and run the converted module --- -try: - mod = runpy.run_path("yolo_converted.py") -except Exception as e: - head = "" - try: - with open("yolo_converted.py", "r", encoding="utf-8") as fh: - head = fh.read(2000) - except Exception: - head = "" - raise RuntimeError(f"Failed to run yolo_converted.py: {e}\n--- head of converted file ---\n{head}") - -# pull the function -detect_and_classify = mod.get("detect_and_classify") -if not detect_and_classify: - raise RuntimeError("detect_and_classify() not found in the extracted cells. Please ensure your notebook defines it.") -print("βœ… detect_and_classify() found and loaded.") +# Use the clean module instead of converting the notebook +from yolo_module import detect_and_classify # --- Load class names (optional, cached to file) --- try: diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt index e5442a3098e0c2d8d2bfa437886a07588d33d0a4..7341e143ad5a23f7cd4b2030398f13f65ba2d444 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt @@ -17,3 +17,5 @@ ipython seaborn gitpython + +opencv-python-headless diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index ccf7f34d94190d8b58132b067d4a701d6663fc3e..df776d573fb006813537930270bfd2e2f7a4b643 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -23,68 +23,133 @@ with open("YOLO.ipynb", "r", encoding="utf-8") as f: nb = nbformat.read(f, as_version=4) # --------------------------- -# Robust: extract only the essential notebook cells and write yolo_converted.py +# Robust dynamic extraction from YOLO.ipynb (safer) # --------------------------- -import nbformat, re, runpy, os, json - -# indices of the important cells found in your YOLO.ipynb -important_cell_indices = [2, 4, 6, 7] +import re, runpy with open("YOLO.ipynb", "r", encoding="utf-8") as f: nb_all = nbformat.read(f, as_version=4) -# safe patterns to remove (Colab magics / upload/demo lines) +# Patterns that indicate a cell is important (we'll include any cell that matches any marker) +IMPORTANT_MARKERS = [ + r"torch\.hub\.load\(", # yolo loader cell + r"models\.resnet18", # classifier architecture + r"model\.load_state_dict", # checkpoint load + r"transform\(", # transform definition + r"transforms\.", # torchvision transforms usage + r"def get_color_name", # color helper + r"def detect_and_classify", # the pipeline function (must be preserved) + r"import numpy", # numpy import + r"import torch", # torch import + r"from torchvision", # torchvision imports + r"from PIL import", # PIL imports + r"import cv2", # optional cv usage +] + +# Lines we will strip from included cells (Colab magics / file upload demos) BAD_LINE_PATTERNS = [ r'^\s*!', # shell commands r'^\s*%', # magics - 'google.colab', - 'files.upload', + r'google\.colab', + r'files.upload', r'\buploaded\b', r'\bimg_path\b', - # note: do NOT ban 'detect_and_classify(' so the function def stays intact ] -collected_cells = [] -for idx in important_cell_indices: - if idx < 0 or idx >= len(nb_all.cells): +# Collect unique cells that match any important marker or imports +selected_cells = [] +seen_indices = set() +for i, cell in enumerate(nb_all.cells): + if cell.cell_type != "code": continue - cell = nb_all.cells[idx] + src = cell.source or "" + # include if any important marker matches + if any(re.search(p, src, flags=re.I) for p in IMPORTANT_MARKERS): + if i not in seen_indices: + seen_indices.add(i) + selected_cells.append((i, src)) + +# If we didn't find the function, as a fallback include any cell that looks like it contains function words +if not any("def detect_and_classify" in src for (_, src) in selected_cells): + for i, cell in enumerate(nb_all.cells): + if cell.cell_type != "code": + continue + src = cell.source or "" + if any(k in src.lower() for k in ["def detect", "def classify", "def predict", "detect_and_classify"]): + if i not in seen_indices: + seen_indices.add(i) + selected_cells.append((i, src)) + +# Also ensure import cells appear first +import_cells = [] +for i, cell in enumerate(nb_all.cells): if cell.cell_type != "code": continue - lines = [] - for line in cell.source.splitlines(): + src = cell.source or "" + if re.search(r'^\s*(import |from )', src, flags=re.M): + if i not in seen_indices: + import_cells.append((i, src)) + seen_indices.add(i) + +# Sort selected cells by original order: imports first, then selected_cells by index +selected_cells_sorted = sorted(import_cells + selected_cells, key=lambda tup: tup[0]) + +# Clean each selected cell, but keep any cell that contains the function header intact. +cleaned_cells = [] +for idx, src in selected_cells_sorted: + # If this cell contains the function definition, keep it largely intact + if "def detect_and_classify" in src: + lines = [] + for line in src.splitlines(): + # remove only Colab magics / shell commands, but keep everything else + if re.match(r'^\s*!', line) or re.match(r'^\s*%', line): + continue + # keep the def line and body exactly as-is otherwise + lines.append(line) + cleaned_src = "\n".join(lines).rstrip() + if cleaned_src: + cleaned_cells.append(cleaned_src) + continue + + # Otherwise, perform conservative cleaning: remove magics, uploads, and *top-level* test calls + cleaned_lines = [] + for line in src.splitlines(): + # drop Colab magics / shell commands and file picker demo lines if any(re.search(p, line) for p in BAD_LINE_PATTERNS): - # skip Colab-only or demo lines inside the important cell continue - lines.append(line) - # only append non-empty cell content - if any(l.strip() for l in lines): - collected_cells.append("\n".join(lines)) + # drop top-level calls to detect_and_classify() in non-function cells (prevents auto-run) + if re.search(r'\bdetect_and_classify\s*\(', line): + continue + cleaned_lines.append(line) + cleaned_src = "\n".join(cleaned_lines).rstrip() + if cleaned_src: + cleaned_cells.append(cleaned_src) -# join with explicit separators (makes debugging easier) -safe_code = "\n\n# ---- cell boundary ----\n\n".join(collected_cells) +# Join cells into script (imports / helpers first by sorted index) +safe_code = "\n\n# ---- cell boundary ----\n\n".join(cleaned_cells) -# small normalization: remove any top-of-file stray "pass # skipped..." left by earlier attempts -safe_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', safe_code, count=1, flags=re.M) -# ensure top-level code does not start with an indented 'pass' -safe_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', safe_code, flags=re.M) - -# Ensure numpy is available (some extracted cells use np) +# Ensure numpy is available if used if not re.search(r'(^|\n)\s*(import numpy\b|import numpy as\b|from numpy\b)', safe_code): safe_code = "import numpy as np\n\n" + safe_code +# Sanitize stray placeholders +safe_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', safe_code, count=1, flags=re.M) +safe_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', safe_code, flags=re.M) + +# Write converted file with open("yolo_converted.py", "w", encoding="utf-8") as fh: fh.write(safe_code) -# quick log (first 800 chars) to help debug in HF logs print("Wrote yolo_converted.py β€” preview:") -print(safe_code[:800].replace("\n", "\\n")) +print(safe_code[:1000].replace("\n", "\\n")) +# --------------------------- +# End dynamic extraction block +# --------------------------- -# Try to load it +# --- Try to import and run the converted module --- try: mod = runpy.run_path("yolo_converted.py") except Exception as e: - # show snippet to help debugging head = "" try: with open("yolo_converted.py", "r", encoding="utf-8") as fh: @@ -96,12 +161,8 @@ except Exception as e: # pull the function detect_and_classify = mod.get("detect_and_classify") if not detect_and_classify: - raise RuntimeError("detect_and_classify() not found in the extracted cells. Please ensure cell indices are correct.") + raise RuntimeError("detect_and_classify() not found in the extracted cells. Please ensure your notebook defines it.") print("βœ… detect_and_classify() found and loaded.") -# --------------------------- -# End extraction block -# --------------------------- - # --- Load class names (optional, cached to file) --- try: diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 3625cd8a64ba5aa03fe20d3f71a637439fa9a4dd..ccf7f34d94190d8b58132b067d4a701d6663fc3e 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -1,103 +1,117 @@ -# -*- coding: utf-8 -*- -"""GradioUI.ipynb - -Automatically generated by Colab. - -Original file is located at - https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp -""" - +# app.py - Hugging Face ready import os -os.system("pip install seaborn --quiet") +import re +import json +import runpy +import nbformat +from nbconvert import PythonExporter +# Prefer dependencies via requirements.txt. Small one-off installs if needed: +# os.system("pip install seaborn --quiet") -import json import torch import gradio as gr from PIL import Image -import nbformat -from nbconvert import PythonExporter -import runpy from datasets import load_dataset -# --- Convert YOLO notebook to Python --- +# --- Ensure YOLO notebook exists --- if not os.path.exists("YOLO.ipynb"): raise FileNotFoundError("YOLO.ipynb not found in app directory!") -# Read YOLO.ipynb -with open("YOLO.ipynb") as f: +# --- Read notebook --- +with open("YOLO.ipynb", "r", encoding="utf-8") as f: nb = nbformat.read(f, as_version=4) -# Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker -# --- Patch the YOLO notebook code to skip testing lines safely --- -for cell in nb.cells: - if cell.cell_type == "code": - lines = [] - for line in cell.source.splitlines(): - bad_patterns = [ - "!", "%", - "google.colab", - "files.upload", - "uploaded", - "img_path", - "detect_and_classify(", - "print(", - "display(" - ] - if any(p in line for p in bad_patterns): - # Keep Python structure valid (avoid empty if-blocks) - lines.append(" pass # skipped during conversion") - continue - lines.append(line) - cell.source = "\n".join(lines) - - -# --- Export cleaned notebook to Python (via nbformat export) --- -py_exporter = PythonExporter() -(code, _) = py_exporter.from_notebook_node(nb) - -# write initial converted file -with open("yolo_converted.py", "w") as f: - f.write(code) - -# --- Post-process the generated file to fix indentation issues from removed lines --- -import re - -with open("yolo_converted.py", "r") as f: - conv_code = f.read() - -# 1) Replace any lines that are only indented 'pass # skipped during conversion' -# with an unindented version so they don't break top-level structure. -conv_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', conv_code, flags=re.M) - -# 2) If any 'pass # skipped during conversion' directly follows a top-level statement -# with incorrect indentation, keep them as 'pass' but ensure indentation matches previous block. -# (This is conservative; we only normalize leading whitespace for the placeholder) -# Already handled by the regex above. - -# 3) Remove any leading 'pass # skipped...' at the very top of the file (if present) -conv_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', conv_code, count=1, flags=re.M) - -# Save cleaned code back -with open("yolo_converted.py", "w") as f: - f.write(conv_code) +# --------------------------- +# Robust: extract only the essential notebook cells and write yolo_converted.py +# --------------------------- +import nbformat, re, runpy, os, json + +# indices of the important cells found in your YOLO.ipynb +important_cell_indices = [2, 4, 6, 7] + +with open("YOLO.ipynb", "r", encoding="utf-8") as f: + nb_all = nbformat.read(f, as_version=4) + +# safe patterns to remove (Colab magics / upload/demo lines) +BAD_LINE_PATTERNS = [ + r'^\s*!', # shell commands + r'^\s*%', # magics + 'google.colab', + 'files.upload', + r'\buploaded\b', + r'\bimg_path\b', + # note: do NOT ban 'detect_and_classify(' so the function def stays intact +] + +collected_cells = [] +for idx in important_cell_indices: + if idx < 0 or idx >= len(nb_all.cells): + continue + cell = nb_all.cells[idx] + if cell.cell_type != "code": + continue + lines = [] + for line in cell.source.splitlines(): + if any(re.search(p, line) for p in BAD_LINE_PATTERNS): + # skip Colab-only or demo lines inside the important cell + continue + lines.append(line) + # only append non-empty cell content + if any(l.strip() for l in lines): + collected_cells.append("\n".join(lines)) + +# join with explicit separators (makes debugging easier) +safe_code = "\n\n# ---- cell boundary ----\n\n".join(collected_cells) + +# small normalization: remove any top-of-file stray "pass # skipped..." left by earlier attempts +safe_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', safe_code, count=1, flags=re.M) +# ensure top-level code does not start with an indented 'pass' +safe_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', safe_code, flags=re.M) + +# Ensure numpy is available (some extracted cells use np) +if not re.search(r'(^|\n)\s*(import numpy\b|import numpy as\b|from numpy\b)', safe_code): + safe_code = "import numpy as np\n\n" + safe_code + +with open("yolo_converted.py", "w", encoding="utf-8") as fh: + fh.write(safe_code) + +# quick log (first 800 chars) to help debug in HF logs +print("Wrote yolo_converted.py β€” preview:") +print(safe_code[:800].replace("\n", "\\n")) + +# Try to load it +try: + mod = runpy.run_path("yolo_converted.py") +except Exception as e: + # show snippet to help debugging + head = "" + try: + with open("yolo_converted.py", "r", encoding="utf-8") as fh: + head = fh.read(2000) + except Exception: + head = "" + raise RuntimeError(f"Failed to run yolo_converted.py: {e}\n--- head of converted file ---\n{head}") -# --- Run the converted YOLO script --- -mod = runpy.run_path("yolo_converted.py") +# pull the function detect_and_classify = mod.get("detect_and_classify") if not detect_and_classify: - raise RuntimeError("detect_and_classify() not found in YOLO.ipynb") + raise RuntimeError("detect_and_classify() not found in the extracted cells. Please ensure cell indices are correct.") +print("βœ… detect_and_classify() found and loaded.") +# --------------------------- +# End extraction block +# --------------------------- -print("βœ… YOLO pipeline loaded successfully") -# --- Load class names --- +# --- Load class names (optional, cached to file) --- try: ds = load_dataset("tanganke/stanford_cars") class_names = ds["train"].features["label"].names - with open("class_names.json", "w") as f: + with open("class_names.json", "w", encoding="utf-8") as f: json.dump(class_names, f) + print(f"βœ… Loaded {len(class_names)} class names") except Exception as e: - print("Warning: Could not load dataset class names.", e) + print("⚠️ Could not load dataset class names:", e) class_names = None # --- Gradio UI --- @@ -106,24 +120,28 @@ def gradio_interface(image): return "Please upload an image." temp_path = "temp_image.png" image.save(temp_path) - try: results = detect_and_classify(temp_path) except Exception as e: return f"❌ Error running YOLO pipeline: {e}" finally: - os.remove(temp_path) + if os.path.exists(temp_path): + os.remove(temp_path) if not results: return "No cars detected." lines = [f"Cars detected: {len(results)}"] for i, item in enumerate(results, start=1): - if len(item) == 4: - crop, pred, color, conf = item - else: - crop, pred, color = item + # item may be (crop, pred_idx, color) or (crop, pred_idx, color, conf) + if isinstance(item, (list, tuple)) and len(item) == 4: + _, pred, color, conf = item + elif isinstance(item, (list, tuple)) and len(item) >= 3: + _, pred, color = item[:3] conf = None + else: + lines.append(f"Car {i}: {item}") + continue if isinstance(pred, int) and class_names and 0 <= pred < len(class_names): name = class_names[pred] diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt index 121b40ebbceea90670196c43f8c38062d07e519f..e5442a3098e0c2d8d2bfa437886a07588d33d0a4 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt @@ -12,3 +12,8 @@ datasets nbformat nbconvert + +ipython + +seaborn +gitpython diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 13ee96246651c1eb8da4d829a871e1524f839b66..3625cd8a64ba5aa03fe20d3f71a637439fa9a4dd 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -52,12 +52,36 @@ for cell in nb.cells: cell.source = "\n".join(lines) -# --- Export cleaned notebook to Python --- +# --- Export cleaned notebook to Python (via nbformat export) --- py_exporter = PythonExporter() (code, _) = py_exporter.from_notebook_node(nb) + +# write initial converted file with open("yolo_converted.py", "w") as f: f.write(code) +# --- Post-process the generated file to fix indentation issues from removed lines --- +import re + +with open("yolo_converted.py", "r") as f: + conv_code = f.read() + +# 1) Replace any lines that are only indented 'pass # skipped during conversion' +# with an unindented version so they don't break top-level structure. +conv_code = re.sub(r'^[ \t]+pass # skipped during conversion\s*$', 'pass # skipped during conversion\n', conv_code, flags=re.M) + +# 2) If any 'pass # skipped during conversion' directly follows a top-level statement +# with incorrect indentation, keep them as 'pass' but ensure indentation matches previous block. +# (This is conservative; we only normalize leading whitespace for the placeholder) +# Already handled by the regex above. + +# 3) Remove any leading 'pass # skipped...' at the very top of the file (if present) +conv_code = re.sub(r'^\s*pass # skipped during conversion\s*', '', conv_code, count=1, flags=re.M) + +# Save cleaned code back +with open("yolo_converted.py", "w") as f: + f.write(conv_code) + # --- Run the converted YOLO script --- mod = runpy.run_path("yolo_converted.py") detect_and_classify = mod.get("detect_and_classify") diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 826abf901224dee96692010a2d6c8cf2267bfde7..13ee96246651c1eb8da4d829a871e1524f839b66 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -29,28 +29,29 @@ with open("YOLO.ipynb") as f: nb = nbformat.read(f, as_version=4) # Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker -# --- Patch the YOLO notebook code to skip testing lines --- +# --- Patch the YOLO notebook code to skip testing lines safely --- for cell in nb.cells: if cell.cell_type == "code": lines = [] for line in cell.source.splitlines(): - if ( - line.strip().startswith("!") or - line.strip().startswith("%") or - "google.colab" in line or - "files.upload" in line or - "uploaded" in line or - "img_path" in line or - "detect_and_classify(" in line or # skip auto test calls - "print(" in line or # skip print-only outputs - "display(" in line # skip Jupyter displays - ): + bad_patterns = [ + "!", "%", + "google.colab", + "files.upload", + "uploaded", + "img_path", + "detect_and_classify(", + "print(", + "display(" + ] + if any(p in line for p in bad_patterns): + # Keep Python structure valid (avoid empty if-blocks) + lines.append(" pass # skipped during conversion") continue lines.append(line) cell.source = "\n".join(lines) - # --- Export cleaned notebook to Python --- py_exporter = PythonExporter() (code, _) = py_exporter.from_notebook_node(nb) diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index d12be3e1327b948ccb07971746da4a9bfd8bc92c..826abf901224dee96692010a2d6c8cf2267bfde7 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -29,21 +29,28 @@ with open("YOLO.ipynb") as f: nb = nbformat.read(f, as_version=4) # Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker +# --- Patch the YOLO notebook code to skip testing lines --- for cell in nb.cells: if cell.cell_type == "code": lines = [] for line in cell.source.splitlines(): - if ( + if ( line.strip().startswith("!") or line.strip().startswith("%") or "google.colab" in line or - "files.upload" in line + "files.upload" in line or + "uploaded" in line or + "img_path" in line or + "detect_and_classify(" in line or # skip auto test calls + "print(" in line or # skip print-only outputs + "display(" in line # skip Jupyter displays ): continue lines.append(line) cell.source = "\n".join(lines) + # --- Export cleaned notebook to Python --- py_exporter = PythonExporter() (code, _) = py_exporter.from_notebook_node(nb) diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 360dec66e91b32aa058305c0955bc626362ea293..d12be3e1327b948ccb07971746da4a9bfd8bc92c 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -8,6 +8,9 @@ Original file is located at """ import os +os.system("pip install seaborn --quiet") + + import json import torch import gradio as gr @@ -25,15 +28,21 @@ if not os.path.exists("YOLO.ipynb"): with open("YOLO.ipynb") as f: nb = nbformat.read(f, as_version=4) -# --- Clean notebook magic commands (!pip, !git, %cd, etc.) --- +# Remove or skip Google Colab imports and magic commands (! or %) or google colab file picker for cell in nb.cells: if cell.cell_type == "code": - cleaned_lines = [] + lines = [] for line in cell.source.splitlines(): - if line.strip().startswith(("!", "%")): + if ( + line.strip().startswith("!") or + line.strip().startswith("%") or + "google.colab" in line or + "files.upload" in line + ): continue - cleaned_lines.append(line) - cell.source = "\n".join(cleaned_lines) + lines.append(line) + cell.source = "\n".join(lines) + # --- Export cleaned notebook to Python --- py_exporter = PythonExporter() diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index f1dd06d003b20f0311a48b52384546ab1cc3488f..360dec66e91b32aa058305c0955bc626362ea293 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -21,14 +21,27 @@ from datasets import load_dataset if not os.path.exists("YOLO.ipynb"): raise FileNotFoundError("YOLO.ipynb not found in app directory!") -# Convert YOLO.ipynb β†’ yolo_converted.py +# Read YOLO.ipynb with open("YOLO.ipynb") as f: nb = nbformat.read(f, as_version=4) + +# --- Clean notebook magic commands (!pip, !git, %cd, etc.) --- +for cell in nb.cells: + if cell.cell_type == "code": + cleaned_lines = [] + for line in cell.source.splitlines(): + if line.strip().startswith(("!", "%")): + continue + cleaned_lines.append(line) + cell.source = "\n".join(cleaned_lines) + +# --- Export cleaned notebook to Python --- py_exporter = PythonExporter() (code, _) = py_exporter.from_notebook_node(nb) with open("yolo_converted.py", "w") as f: f.write(code) +# --- Run the converted YOLO script --- mod = runpy.run_path("yolo_converted.py") detect_and_classify = mod.get("detect_and_classify") if not detect_and_classify: diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt index 4c97ff3549d12b1bd5d824e79fa8b6dec7770594..121b40ebbceea90670196c43f8c38062d07e519f 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt @@ -9,3 +9,6 @@ opencv-python timm transformers datasets + +nbformat +nbconvert diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 86ca614675d5f5fe054e68ec7a0485237e714fcb..f1dd06d003b20f0311a48b52384546ab1cc3488f 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -7,146 +7,75 @@ Original file is located at https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp """ +import os +import json +import torch import gradio as gr from PIL import Image -import torch -import os - -import os -from google.colab import files - -if not os.path.exists('YOLO.ipynb'): - print("Please upload YOLO.ipynb (the script exported from your YOLO notebook).") - uploaded = files.upload() # upload YOLO.ipynb - print("Uploaded:", list(uploaded.keys())) -else: - print("YOLO.ipynb already present.") - -!ls /content - import nbformat from nbconvert import PythonExporter import runpy +from datasets import load_dataset -# Convert YOLO.ipynb to a .py script dynamically +# --- Convert YOLO notebook to Python --- +if not os.path.exists("YOLO.ipynb"): + raise FileNotFoundError("YOLO.ipynb not found in app directory!") + +# Convert YOLO.ipynb β†’ yolo_converted.py with open("YOLO.ipynb") as f: nb = nbformat.read(f, as_version=4) - -# Find the cell that loads car_classifier.pth and modify the path -# Also, remove any code that tries to open the notebook file as an image -modified_cells = [] -for cell in nb.cells: - if cell.cell_type == 'code': - # This is a heuristic: look for lines containing 'car_classifier.pth' - if 'car_classifier.pth' in cell.source: - cell.source = cell.source.replace("'car_classifier.pth'", "'/content/car_classifier.pth'") - cell.source = cell.source.replace('"car_classifier.pth"', '"/content/car_classifier.pth"') - - # Heuristic to remove code that might try to open the notebook as an image - if 'Image.open(' in cell.source and 'YOLO.ipynb' in cell.source: - cell.source = '# Removed potential image loading of notebook file:\n#' + cell.source - py_exporter = PythonExporter() (code, _) = py_exporter.from_notebook_node(nb) - -# Save temporarily as script with open("yolo_converted.py", "w") as f: f.write(code) -# Now safely import detect_and_classify() from that converted script mod = runpy.run_path("yolo_converted.py") detect_and_classify = mod.get("detect_and_classify") - if not detect_and_classify: - raise RuntimeError("Function detect_and_classify not found in YOLO.ipynb") + raise RuntimeError("detect_and_classify() not found in YOLO.ipynb") -print("βœ… YOLO function imported successfully") +print("βœ… YOLO pipeline loaded successfully") -import torch, os, json -pth = "/content/car_classifier.pth" -print("Exists:", os.path.exists(pth)) -ckpt = torch.load(pth, map_location="cpu") -print("Type:", type(ckpt)) - -if isinstance(ckpt, dict): - keys = list(ckpt.keys()) - print("Checkpoint keys (first 20):", keys[:20]) - # If it's a pure state_dict, it will look like parameter names (e.g. 'conv1.weight') - # If it's a wrapped checkpoint, it may contain 'model_state_dict' or 'class_names' -else: - print("Checkpoint is not a dict; it's probably a raw model object.") - -!pip install -q datasets - -from datasets import load_dataset -ds = load_dataset("tanganke/stanford_cars") -# HF dataset provides label names in the train feature -class_names = ds["train"].features["label"].names -print("Loaded", len(class_names), "class names. Sample:", class_names[:10]) - -# Save to disk for reuse -import json -with open("class_names.json", "w") as f: - json.dump(class_names, f, indent=2) -print("Saved class_names.json") - -import json, os -if os.path.exists("class_names.json"): - with open("class_names.json") as f: - class_names = json.load(f) - print("Loaded class_names from file, len =", len(class_names)) -else: - print("class_names.json not found; run the HF cell above.") - -import gradio as gr -import os - -# ensure class_names exists in the notebook (from previous cell) +# --- Load class names --- try: - assert class_names is not None and len(class_names) > 0 - print("Using class_names with", len(class_names), "entries") -except Exception: + ds = load_dataset("tanganke/stanford_cars") + class_names = ds["train"].features["label"].names + with open("class_names.json", "w") as f: + json.dump(class_names, f) +except Exception as e: + print("Warning: Could not load dataset class names.", e) class_names = None - print("class_names not available; will show numeric labels") -def gradio_interface(image, *args, **kwargs): +# --- Gradio UI --- +def gradio_interface(image): if image is None: return "Please upload an image." - temp_path = "temp_image.png" image.save(temp_path) try: - results = detect_and_classify(temp_path) # your notebook function + results = detect_and_classify(temp_path) except Exception as e: return f"❌ Error running YOLO pipeline: {e}" finally: - if os.path.exists(temp_path): - os.remove(temp_path) + os.remove(temp_path) if not results: return "No cars detected." lines = [f"Cars detected: {len(results)}"] - for i, item in enumerate(results, start=1): - # handle both 3-tuple and 4-tuple safely if len(item) == 4: crop, pred, color, conf = item else: crop, pred, color = item conf = None - # map pred -> human name if possible - if isinstance(pred, int): - if class_names and 0 <= pred < len(class_names): - name = class_names[pred] - else: - name = f"Class {pred}" + if isinstance(pred, int) and class_names and 0 <= pred < len(class_names): + name = class_names[pred] else: name = str(pred) - # Format with confidence if available if conf is not None: lines.append(f"Car {i}: {color} {name} ({conf*100:.1f}% confident)") else: @@ -154,28 +83,13 @@ def gradio_interface(image, *args, **kwargs): return "\n".join(lines) -# Launch Gradio Interface iface = gr.Interface( fn=gradio_interface, inputs=gr.Image(type="pil", label="Upload an Image"), outputs=gr.Textbox(label="Detection & Classification Results"), title="Car Detector + Classifier (YOLO)", - description="Upload a car image and get its color, model, and confidence score.", + description="Upload a car image and get its color, model, and confidence score." ) -iface.launch(share=True) - -# Test the gradio_interface function with the venza.jpg image -image_path = "/content/venza.jpg" -try: - # Open the image file - image = Image.open(image_path) - # Call the gradio_interface function, passing class_names - test_output = gradio_interface(image, class_names) - # Print the output - print(test_output) -except FileNotFoundError: - print(f"Error: Image file not found at {image_path}") -except Exception as e: - print(f"An error occurred: {e}") -!grep -n "results" YOLO.ipynb +if __name__ == "__main__": + iface.launch() diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py index 2561ffd79d627fe7d808b1859b2e8aede3bb536e..86ca614675d5f5fe054e68ec7a0485237e714fcb 100644 --- a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -7,9 +7,9 @@ Original file is located at https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp """ -!pip install gradio --quiet import gradio as gr from PIL import Image +import torch import os import os @@ -178,4 +178,4 @@ 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done.\u001b[K\n", + "remote: Counting objects: 100% (3/3), done.\u001b[K\n", + "remote: Compressing objects: 100% (3/3), done.\u001b[K\n", + "remote: Total 17582 (delta 0), reused 0 (delta 0), pack-reused 17579 (from 2)\u001b[K\n", + "Receiving objects: 100% (17582/17582), 16.86 MiB | 15.17 MiB/s, done.\n", + "Resolving deltas: 100% (11973/11973), done.\n", + "/content/yolov5\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5\n", + "!pip install -r requirements.txt -q\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torchvision.transforms as transforms\n", + "from torchvision import models\n", + "import torch.nn as nn\n" + ], + "metadata": { + "id": "nxvyND2CEfr9" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "yolo = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QtJ2iv99Elwx", + "outputId": "d7db2ce9-72b7-45fb-872f-81a893f303eb" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torch/hub.py:330: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /root/.cache/torch/hub/master.zip\n", + "Creating new Ultralytics Settings v0.0.6 file βœ… \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 πŸš€ 2025-10-9 Python-3.12.11 torch-2.8.0+cu126 CPU\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", + "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 14.1M/14.1M [00:00<00:00, 93.4MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n", + "Adding AutoShape... \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "uploaded = files.upload() # pick car_classifier.pth from your computer\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + }, + "id": "1Y_1Fr33GXOm", + "outputId": "820588a2-471f-44d8-b9ed-1d9d15ff0dab" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving car_classifier.pth to car_classifier.pth\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os, torch\n", + "from torchvision import models, transforms\n", + "import torch.nn as nn\n", + "\n", + "# Rebuild architecture\n", + "model = models.resnet18(pretrained=False)\n", + "model.fc = nn.Linear(model.fc.in_features, 196) # 196 car classes\n", + "\n", + "# Find model file dynamically\n", + "model_path = \"car_classifier.pth\"\n", + "\n", + "# fallback: look inside \"content\" folder if not found\n", + "if not os.path.exists(model_path):\n", + " alt_path = os.path.join(\"content\", \"car_classifier.pth\")\n", + " if os.path.exists(alt_path):\n", + " model_path = alt_path\n", + " else:\n", + " raise FileNotFoundError(\"❌ Could not find car_classifier.pth in either root or /content\")\n", + "\n", + "print(f\"βœ… Loading model from: {model_path}\")\n", + "model.load_state_dict(torch.load(model_path, map_location=\"cpu\"))\n", + "model.eval()\n", + "\n", + "# Define transform (for cropped car images)\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VJc8_THiEpyu", + "outputId": "10b91dc6-22f6-4d30-e4c8-452017637cbc" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… Loading model from: car_classifier.pth\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Stanford Cars class names (from dataset metadata)\n", + "from datasets import load_dataset\n", + "ds_meta = load_dataset(\"tanganke/stanford_cars\")\n", + "class_names = ds_meta[\"train\"].features[\"label\"].names\n" + ], + "metadata": { + "id": "m7uj1CAGI1DR", + "colab": { + "base_uri": 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and g < 50 and b < 50:\n", + " return \"Black\"\n", + " elif r > 200 and g > 200 and b < 100:\n", + " return \"Yellow\"\n", + " else:\n", + " return \"Gray/Silver\"\n" + ], + "metadata": { + "id": "Sux5PDyiEs46" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def detect_and_classify(img_path):\n", + " results = yolo(img_path)\n", + " detections = results.xyxy[0]\n", + " # confidences = results.boxes.conf.cpu().numpy().tolist() if hasattr(results, 'boxes') else []\n", + "\n", + " img = Image.open(img_path).convert(\"RGB\")\n", + " preds = []\n", + " for *box, conf, cls in detections:\n", + " if int(cls) == 2: # YOLO COCO class 2 = car\n", + " x1, y1, x2, y2 = map(int, box)\n", + " crop = img.crop((x1, y1, x2, y2))\n", + "\n", + " # classify\n", + " tensor = transform(crop).unsqueeze(0)\n", + " with torch.no_grad():\n", + " output = model(tensor)\n", + " pred_class = output.argmax(1).item()\n", + "\n", + " # get color\n", + " color = get_color_name(crop)\n", + "\n", + " # normalize/convert confidence to float in range ~0..1 (safe handling)\n", + " try:\n", + " conf_val = float(conf)\n", + " # if the confidence somehow came as percentage (0-100), convert to 0-1\n", + " if conf_val > 1.5:\n", + " conf_val = conf_val / 100.0\n", + " except Exception:\n", + " conf_val = None\n", + "\n", + " # append a 4-tuple now: (crop, predicted_class_index, color_name, confidence_float)\n", + " preds.append((crop, pred_class, color, conf_val))\n", + "\n", + " return preds\n" + ], + "metadata": { + "id": "JGOpRtYLExTD" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "import matplotlib.pyplot as plt\n", + "uploaded = files.upload()\n", + "\n", + "img_path = list(uploaded.keys())[0]\n", + "results = detect_and_classify(img_path)\n", + "\n", + "print(\"Cars detected:\", len(results))\n", + "for idx, (crop, pred, color, conf) in enumerate(results):\n", + " # map to human name if class_names available\n", + " if 'class_names' in globals() and class_names is not None and isinstance(pred, int) and 0 <= pred < len(class_names):\n", + " car_name = class_names[pred]\n", + " else:\n", + " car_name = f\"Class {pred}\"\n", + "\n", + " # print with confidence if available\n", + " if conf is not None:\n", + " print(f\"Car {idx+1}: {color} {car_name} ({conf*100:.1f}% confident)\")\n", + " else:\n", + " print(f\"Car {idx+1}: {color} {car_name}\")\n", + "\n", + " # show the crop with the same title\n", + " plt.subplot(1, len(results), idx+1)\n", + " plt.imshow(crop)\n", + " title = f\"{color} {car_name}\"\n", + " if conf is not None:\n", + " title += f\"\\n({conf*100:.1f}% confident)\"\n", + " plt.title(title)\n", + " plt.axis(\"off\")\n", + "\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 162 + }, + "id": "UD1HuD0NE0KQ", + "outputId": "20004475-e9ad-4fef-e154-be10b0a36cf4" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving venza.jpg to venza (2).jpg\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/root/.cache/torch/hub/ultralytics_yolov5_master/models/common.py:906: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", + " with amp.autocast(autocast):\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cars detected: 1\n", + "Car 1: Gray/Silver Dodge Dakota Crew Cab 2010 (45.9% confident)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2dx_3gUObSs6" + }, + "execution_count": 9, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/car_classifier.pth 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a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/README.md b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a37ce2635d29ab40ecb81e234d09fe48637f8bf5 --- /dev/null +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/README.md @@ -0,0 +1,91 @@ +--- +title: Car Classifier Model +emoji: πŸš— +colorFrom: blue +colorTo: purple +sdk: gradio +sdk_version: "4.0.0" +app_file: app.py +pinned: false +--- + +# AiModelCarClassifier +Creating and Running a Car Classifier Model... + +## Car Detector(YOLO + Custom Model) + +This project uses **YOLOv5** for car detection and a **custom-trained classifier** for car model recognition and color identification. +It takes in any image (JPEG/PNG), detects cars, classifies the car make & model, and outputs color and confidence scores. + +Example output: +- **Cars detected: 1** +- **Car 1: Gray/Silver Dodge Dakota Crew Cab 2010 (98.7% confident)** + +--- + +## Overview + +This project combines **object detection** and **image classification** in one simple pipeline: + +1. **YOLOv5** detects cars in the image. +2. The detected car regions are cropped and passed into a **PyTorch classifier** (`car_classifier.pth`). +3. A small color recognition helper determines the car’s dominant color. +4. Results are displayed through a simple **Gradio UI** (or any frontend, e.g. HTML + Flask). + +--- + +## Project Structure + +β”‚ +β”œβ”€β”€ YOLO.ipynb # Main notebook for YOLO + classification logic +β”œβ”€β”€ car_classifier.pth # Trained PyTorch model for car model recognition +β”œβ”€β”€ app.py # Gradio (or Flask) app for running the interface +β”œβ”€β”€ class_names.json # (Optional) Human-readable class labels +β”œβ”€β”€ requirements.txt # Python dependencies +└── README.md # Project description + +--- + +--- + +## Works steps + +1. **Image Upload** β†’ User uploads an image. +2. **YOLOv5 Detection** β†’ Detects car bounding boxes. +3. **Classification** β†’ Each car crop is classified using `car_classifier.pth`. +4. **Color Recognition** β†’ Extracts car color from the cropped region. +5. **Output** β†’ Displays model name, color, and confidence percentage. + +--- +## Model Details + +- **YOLOv5**: Handles object detection (pretrained on COCO dataset). +- **Car Classifier (`car_classifier.pth`)**: Fine-tuned model trained on [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars). +- **Color Extractor**: Uses average RGB values to estimate color. + +--- +## install depencies +``` +pip install -r requirements.txt +``` + + +Then open the Gradio or local web interface that appears in your console. +--- + +## Setup & Run + +Clone the repo: + +Then open the Gradio or local web interface that appears in your console. +```bash +https://github.com//AiModelCarClassifier.git +cd car-detector-classifier +``` + + + +## run the app +``` +python app.py +``` diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py new file mode 100644 index 0000000000000000000000000000000000000000..2561ffd79d627fe7d808b1859b2e8aede3bb536e --- /dev/null +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/app.py @@ -0,0 +1,181 @@ +# -*- coding: utf-8 -*- +"""GradioUI.ipynb + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1gTrf304mzjGMheD47oHDhnYTIrEyf4qp +""" + +!pip install gradio --quiet +import gradio as gr +from PIL import Image +import os + +import os +from google.colab import files + +if not os.path.exists('YOLO.ipynb'): + print("Please upload YOLO.ipynb (the script exported from your YOLO notebook).") + uploaded = files.upload() # upload YOLO.ipynb + print("Uploaded:", list(uploaded.keys())) +else: + print("YOLO.ipynb already present.") + +!ls /content + +import nbformat +from nbconvert import PythonExporter +import runpy + +# Convert YOLO.ipynb to a .py script dynamically +with open("YOLO.ipynb") as f: + nb = nbformat.read(f, as_version=4) + +# Find the cell that loads car_classifier.pth and modify the path +# Also, remove any code that tries to open the notebook file as an image +modified_cells = [] +for cell in nb.cells: + if cell.cell_type == 'code': + # This is a heuristic: look for lines containing 'car_classifier.pth' + if 'car_classifier.pth' in cell.source: + cell.source = cell.source.replace("'car_classifier.pth'", "'/content/car_classifier.pth'") + cell.source = cell.source.replace('"car_classifier.pth"', '"/content/car_classifier.pth"') + + # Heuristic to remove code that might try to open the notebook as an image + if 'Image.open(' in cell.source and 'YOLO.ipynb' in cell.source: + cell.source = '# Removed potential image loading of notebook file:\n#' + cell.source + +py_exporter = PythonExporter() +(code, _) = py_exporter.from_notebook_node(nb) + +# Save temporarily as script +with open("yolo_converted.py", "w") as f: + f.write(code) + +# Now safely import detect_and_classify() from that converted script +mod = runpy.run_path("yolo_converted.py") +detect_and_classify = mod.get("detect_and_classify") + +if not detect_and_classify: + raise RuntimeError("Function detect_and_classify not found in YOLO.ipynb") + +print("βœ… YOLO function imported successfully") + +import torch, os, json +pth = "/content/car_classifier.pth" +print("Exists:", os.path.exists(pth)) +ckpt = torch.load(pth, map_location="cpu") +print("Type:", type(ckpt)) + +if isinstance(ckpt, dict): + keys = list(ckpt.keys()) + print("Checkpoint keys (first 20):", keys[:20]) + # If it's a pure state_dict, it will look like parameter names (e.g. 'conv1.weight') + # If it's a wrapped checkpoint, it may contain 'model_state_dict' or 'class_names' +else: + print("Checkpoint is not a dict; it's probably a raw model object.") + +!pip install -q datasets + +from datasets import load_dataset +ds = load_dataset("tanganke/stanford_cars") +# HF dataset provides label names in the train feature +class_names = ds["train"].features["label"].names +print("Loaded", len(class_names), "class names. Sample:", class_names[:10]) + +# Save to disk for reuse +import json +with open("class_names.json", "w") as f: + json.dump(class_names, f, indent=2) +print("Saved class_names.json") + +import json, os +if os.path.exists("class_names.json"): + with open("class_names.json") as f: + class_names = json.load(f) + print("Loaded class_names from file, len =", len(class_names)) +else: + print("class_names.json not found; run the HF cell above.") + +import gradio as gr +import os + +# ensure class_names exists in the notebook (from previous cell) +try: + assert class_names is not None and len(class_names) > 0 + print("Using class_names with", len(class_names), "entries") +except Exception: + class_names = None + print("class_names not available; will show numeric labels") + +def gradio_interface(image, *args, **kwargs): + if image is None: + return "Please upload an image." + + temp_path = "temp_image.png" + image.save(temp_path) + + try: + results = detect_and_classify(temp_path) # your notebook function + except Exception as e: + return f"❌ Error running YOLO pipeline: {e}" + finally: + if os.path.exists(temp_path): + os.remove(temp_path) + + if not results: + return "No cars detected." + + lines = [f"Cars detected: {len(results)}"] + + for i, item in enumerate(results, start=1): + # handle both 3-tuple and 4-tuple safely + if len(item) == 4: + crop, pred, color, conf = item + else: + crop, pred, color = item + conf = None + + # map pred -> human name if possible + if isinstance(pred, int): + if class_names and 0 <= pred < len(class_names): + name = class_names[pred] + else: + name = f"Class {pred}" + else: + name = str(pred) + + # Format with confidence if available + if conf is not None: + lines.append(f"Car {i}: {color} {name} ({conf*100:.1f}% confident)") + else: + lines.append(f"Car {i}: {color} {name}") + + return "\n".join(lines) + +# Launch Gradio Interface +iface = gr.Interface( + fn=gradio_interface, + inputs=gr.Image(type="pil", label="Upload an Image"), + outputs=gr.Textbox(label="Detection & Classification Results"), + title="Car Detector + Classifier (YOLO)", + description="Upload a car image and get its color, model, and confidence score.", +) +iface.launch(share=True) + +# Test the gradio_interface function with the venza.jpg image +image_path = "/content/venza.jpg" +try: + # Open the image file + image = Image.open(image_path) + # Call the gradio_interface function, passing class_names + test_output = gradio_interface(image, class_names) + # Print the output + print(test_output) +except FileNotFoundError: + print(f"Error: Image file not found at {image_path}") +except Exception as e: + print(f"An error occurred: {e}") + +!grep -n "results" YOLO.ipynb \ No newline at end of file diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e643e8879cdaf028ee51d005917718d0d087fcab --- /dev/null +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/space_repo/requirements.txt @@ -0,0 +1 @@ +trackio<1.0 \ No newline at end of file diff --git a/space_repo/space_repo/space_repo/space_repo/space_repo/yolo_module.py b/space_repo/space_repo/space_repo/space_repo/space_repo/yolo_module.py new file mode 100644 index 0000000000000000000000000000000000000000..dff01c29f4cb478d0f9b9f70b876f7b049f77b4f --- /dev/null +++ b/space_repo/space_repo/space_repo/space_repo/space_repo/yolo_module.py @@ -0,0 +1,173 @@ +# yolo_module.py +# A small, standalone wrapper for YOLOv5 detection + a saved PyTorch classifier. +# Designed to be imported by app.py (Hugging Face / Gradio). + +import os +from PIL import Image +import numpy as np +import torch +import torch.nn.functional as F +from torchvision import transforms, models +import torch.nn as nn + +# Load YOLOv5 model (uses torch.hub β€” Ultralytics repo) +# NOTE: this will download yolov5s.pt the first time (cached in environment). +yolo = None +try: + yolo = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) +except Exception as e: + # If users want to use ultralytics package or different method, handle gracefully. + print("Warning: could not load yolov5 via torch.hub:", e) + yolo = None + +# -- Classifier model (ResNet18, 196 classes) -- +model = None +transform = None +def _load_classifier(): + global model, transform + # architecture + model = models.resnet18(pretrained=False) + model.fc = nn.Linear(model.fc.in_features, 196) + # find the checkpoint saved in repo or /content folder + model_path = "car_classifier.pth" + if not os.path.exists(model_path): + alt = os.path.join("content", "car_classifier.pth") + if os.path.exists(alt): + model_path = alt + if not os.path.exists(model_path): + # If missing, we keep model=None and later return an error + print("Warning: car_classifier.pth not found at root or /content. Classifier disabled.") + model = None + transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor()]) + return + + ckpt = torch.load(model_path, map_location="cpu") + # ckpt might be a full dict or a state_dict β€” handle both cases + if isinstance(ckpt, dict): + # common keys: "model_state_dict" or bare state_dict + if "model_state_dict" in ckpt: + state = ckpt["model_state_dict"] + elif any(k.startswith('conv1') for k in ckpt.keys()): + state = ckpt + else: + # unknown dict structure β€” try to find a nested state dict + possible = None + for v in ckpt.values(): + if isinstance(v, dict) and any(k.startswith('conv1') for k in v.keys()): + possible = v + break + state = possible or ckpt + else: + # ckpt directly is probably a state_dict + state = ckpt + + try: + model.load_state_dict(state) + model.eval() + print("βœ… Loaded classifier from", model_path) + except Exception as e: + print("Warning: failed to load state_dict cleanly:", e) + model = None + + transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + ]) + +# Try to load on import +_load_classifier() + + +# Simple color extractor (dominant-ish color) +def get_color_name(image_pil): + try: + img = image_pil.resize((50, 50)) + arr = np.array(img).reshape(-1, 3) + avg = arr.mean(axis=0) + r, g, b = avg + # simple thresholds + if r > 150 and g < 100 and b < 100: + return "Red" + if b > 150 and r < 100 and g < 100: + return "Blue" + if g > 150 and r < 100 and b < 100: + return "Green" + if r > 200 and g > 200 and b > 200: + return "White" + if r < 50 and g < 50 and b < 50: + return "Black" + if r > 200 and g > 200 and b < 100: + return "Yellow" + return "Gray/Silver" + except Exception: + return "Unknown" + +# The pipeline function expected by app.py +def detect_and_classify(img_path): + """ + Input: img_path (str) + Output: list of tuples (PIL.Image crop, pred_class_idx (int), color_name (str), classifier_confidence (float or None)) + If classifier not available, pred_class_idx may be integer index (if you still have names elsewhere) or None. + """ + if not os.path.exists(img_path): + raise FileNotFoundError(f"Image not found: {img_path}") + + # If YOLO not available, return helpful error + if yolo is None: + raise RuntimeError("YOLO model not loaded (yolo is None). Check logs for earlier warning.") + + img = Image.open(img_path).convert("RGB") + # Run YOLO detection + results = yolo(img_path) # Ultralytics API: passing path or PIL works + + # results.xyxy[0] is an Nx6 array: x1,y1,x2,y2,conf,cls + try: + dets = results.xyxy[0].cpu().numpy() + except Exception: + # fallback: try to convert via .pandas().xyxy[0] + try: + dets = results.pandas().xyxy[0].values + except Exception: + dets = [] + + preds = [] + for det in dets: + try: + x1, y1, x2, y2, conf_det, cls = det + except Exception: + # if det is dict-like from pandas + try: + x1 = float(det[0]); y1 = float(det[1]); x2 = float(det[2]); y2 = float(det[3]) + conf_det = float(det[4]); cls = float(det[5]) + except Exception: + continue + + if int(cls) != 2: # COCO class 2 == car + continue + + # crop with PIL (ensure integer coords and within bounds) + x1i, y1i, x2i, y2i = map(int, [max(0, x1), max(0, y1), max(0, x2), max(0, y2)]) + crop = img.crop((x1i, y1i, x2i, y2i)) + + # classifier + class_idx = None + class_conf = None + if model is not None: + try: + t = transform(crop).unsqueeze(0) # batch 1 + with torch.no_grad(): + out = model(t) + probs = F.softmax(out, dim=1) + class_conf = float(probs.max().item()) + class_idx = int(probs.argmax().item()) + except Exception as e: + # if classifier fails, leave class_idx None + class_idx = None + class_conf = None + + # color + color = get_color_name(crop) + + preds.append((crop, class_idx, color, class_conf)) + + return preds